ArticleiScience2026
Music recognition based on diffractive neural networks.
Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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3 authors.
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Abstract
Diffractive neural networks offer high-speed and energy-efficient computation for artificial intelligence hardware, yet their application to time-series signal processing remains underexplored. Here, we propose an electromagnetic diffractive neural network framework for music style and sentiment classification. Time-series audio signals are converted into log-Mel spectrograms and processed using a multilayer metasurface-based DNN. Experiments on the GTZAN dataset achieved classification accuracies of 90.3% for five genres and 96.33% for three genres. Furthermore, sentiment classification on the POP909 dataset achieved 90.56% accuracy. These results demonstrate that diffractive neural networks can effectively process time-series data and provide a promising hardware-efficient solution for intelligent signal processing in communication and sensing applications.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.